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Urea/SCR systems have been proven effective at reducing NOx over a wide range of operating conditions on mid/heavy duty diesel vehicles. However, design changes due to reduction in the size of modern compact Urea/SCR systems and lower exhaust temperature have increased the possibility of urea deposit formation. Urea deposits are formed when urea in films and droplets undergoes undesirable secondary reactions and generate by-products such as ammelide, biuret and cyanuric Acid (CYA). Ammelide and CYA are difficult to decompose which lead to the formation of solid deposits on the surface. This phenomenon degrades the performance of the after treatment system by decreasing overall mixing efficiency, lowering de-NOx efficiency and increasing pressure drop. Therefore, mitigating urea deposits is a primary design goal of modern diesel after-treatment systems.

For commercial-vehicle Original Equipment Manufacturers (OEMs), predictive maintenance has drawn attention for the benefits of money saving and increased road safety. Data-driven models have been widely explored and implemented as predictive maintenance solutions. However, the working scenarios for different commercial-vehicles vary a lot, which makes it difficult to build a universal model suitable for all the cases. In this paper, we propose a Recurrent Neural Adaptive Processes (RNAPs) network to adapt to different scenarios by modeling the uncertain at the same time. The ensemble network combines the traits of neural processes, recurrent neural network and meta learning together. Neural processes consider the context information to calculate the uncertainty and improve the prediction results. Meta-learning works well when dealing with few-shot multi-tasks learning, and recurrent networks are utilized as the encoder of the proposed model to process time-series data.

In the current study, an automatic and interpretable predictive maintenance system is proposed. The system provides a fully automatic training process for predictive maintenance models without human intervention. On the other hand, as failure reasons are critical for product development. The proposed pipeline also demonstrates the interpretation on automatic trained model to present insights for engineers to acquire mechanism of interested events. To study the system, four automatic machine learning methods and two interpretation modules are evaluated for the pipeline with Isuzu’ real vehicle data correspondingly. The overall performance of the automatic and interpretable system is demonstrated as well. Key words: predictive maintenance, AutoML, interpretation

Field Issue (Malfunction) incidents are costly for the manufacturer’s service department. Especially for commercial truck providers, downtime can be the biggest concern for our customers. To reduce warranty cost and improve customer confidence in our products, preventive maintenance provides the benefit of fixing the problem when it is small and reducing downtime of scheduled targeted service time. However, a normal telematics system has difficulty in capturing useful information even with pre-set triggers. Some malfunction issue takes weeks to find the root cause due to the difficulty of repeating the error in a different vehicle and engineers must analyze large amounts of data. In order to solve these challenges, a machine-learning-based predictive software/hardware system has been implemented.